Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Implantable Cardioverter-Defibrillator Therapy in Contemporary Heart Failure Patients: An Analysis From the EMPEROR-Reduced Trial.

JACC. Heart failure·2026
Same author

Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.

The journal of prevention of Alzheimer's disease·2026
Same author

Proteomic patterns according to ejection fraction: an EMPEROR-programme analysis.

European journal of heart failure·2026
Same author

A spectral dimension reduction technique that improves pattern detection in multivariate spatial data.

Bioinformatics (Oxford, England)·2026
Same author

Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Identification of novel fibroblast subsets in diffuse cutaneous systemic sclerosis.

Annals of the rheumatic diseases·2025
Same journal

Learning directed acyclic graphs from large-scale genomics data.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Bayesian inference for biomarker discovery in proteomics: an analytic solution.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Review of stochastic hybrid systems with applications in biological systems modeling and analysis.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

On biometric systems: electrocardiogram Gaussianity and data synthesis.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Biomedical informatics with optimization and machine learning.

EURASIP journal on bioinformatics & systems biology·2017
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Bayesian network view on nested effects models.

Cordula Zeller1, Holger Fröhlich, Achim Tresch

  • 1Department of Mathematics, Johannes Gutenberg University, Mainz, Germany.

EURASIP Journal on Bioinformatics & Systems Biology
|January 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible Bayesian network formulation for Nested Effects Models (NEMs) to uncover hidden biological signaling pathways. The new methods, implemented in the R/Bioconductor package nem, improve pathway reconstruction and analysis.

Related Experiment Videos

Last Updated: Jun 26, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Nested Effects Models (NEMs) are used to infer hidden signaling structures from observed effects of interventions.
  • Existing NEMs rely on implicit assumptions, limiting flexibility.

Purpose of the Study:

  • To develop a more flexible formulation of NEMs using Bayesian networks.
  • To explicitly state underlying assumptions of NEMs.
  • To introduce novel learning algorithms for NEMs.

Main Methods:

  • Formulation of NEMs within the Bayesian network framework.
  • Development of new learning algorithms for the generalized NEMs.
  • Implementation of methods in the R/Bioconductor package 'nem'.
  • Validation through simulation studies and application to a yeast synthetic lethality dataset.

Main Results:

  • The Bayesian network approach provides a generalized and more flexible framework for NEMs.
  • New learning methods for NEMs demonstrate effectiveness in simulations.
  • Successful application to a real-world biological dataset (yeast synthetic lethality).

Conclusions:

  • The proposed Bayesian network formulation enhances the capabilities of Nested Effects Models.
  • The new learning methods offer improved tools for reconstructing biological signaling pathways.
  • The 'nem' package provides a practical implementation for researchers.